Download Optimizing the Knowledge Discovery Process - CEUR

Optimizing the Knowledge Discovery Process
through Semantic Meta-Mining
Melanie Hilario
Computer Science Department
University of Geneva
Geneva, Switzerland
Abstract. I will describe a novel meta-learning approach to optimizing
the knowledge discovery or data mining (DM) process. This approach has
three features that distinguish it from its predecessors. First, previous
meta-learning research has focused exclusively on improving the learning
phase of the DM process. More specifically, the goal of meta-learning has
typically been to select the most appropriate algorithm and/or parameter settings for a given learning task. We adopt a more process-oriented
approach whereby meta-learning is applied to design choices at different
stages of the complete data mining process or workflow (hence the term
meta-mining). Second, meta-learning for algorithm or model selection
has consisted mainly in mapping dataset properties to the observed performance of algorithms viewed as black boxes. While several generations
of researchers have worked intensively on characterizing datasets, little
has been done to understand the internal mechanisms of the algorithms
used. At best, a few have considered perceptible features of algorithms
like their ease of implementation or their robustness to noise, or the interpretability of the models they produce. In contrast, our meta-learning
approach complements dataset descriptions with an in-depth analysis
and characterization of algorithms - their underlying assumptions, optimization goals and strategies, together with the structure and complexity
of the models and patterns they generate. Third, previous meta-learning
approaches have been strictly (meta) data-driven. To make sense of the
intricate relationships between tasks, data and algorithms at different
stages of the data mining process, our meta-miner relies on extensive
background knowledge concerning knowledge discovery itself. For this
reason we have developed a data mining ontology, which defines the essential concepts and relations needed to represent and analyse data mining objects and processes. In addition, a DM knowledge base gathers assertions concerning data preprocessing and machine learning algorithms
as well as their implementations in several open-source software packages. The DM ontology and knowledge base are domain-independent;
they can be exploited in any application area to build databases describing domain-specific data analysis tasks, datasets and experiments.
Aside from their direct utility in their respective target domains, such
databases are the indispensable source of training and evaluation data
for the meta-miner. These three features together lay the groundwork for
semantic meta-mining, the process of mining DM meta-data on the basis
of data mining expertise distilled in an ontology and knowledge base.